Mapreduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems
Language: English
Published by Oreilly & Associates Inc, 2012
- Softcover
- New
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
AbeBooks seller since January 6, 2003
Condition: New
£ 41.79
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Add to basketItem description from seller
1st edition. 232 pages. 9.25x7.00x0.75 inches. In Stock. This item is printed on demand.
Seller Inventory # __1449327176
- Title
- Mapreduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems
- Author
- Miner, Donald/ Shook, Adam
- Publisher
- Oreilly & Associates Inc
- Publication year
- 2012
- Condition
- Brand New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1449327176
- ISBN 13
- 9781449327170
- Item weight
- 0.45 kilograms
Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you're using.
Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop.
- Summarization patterns: get a top-level view by summarizing and grouping data
- Filtering patterns: view data subsets such as records generated from one user
- Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier
- Join patterns: analyze different datasets together to discover interesting relationships
- Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job
- Input and output patterns: customize the way you use Hadoop to load or store data
"A clear exposition of MapReduce programs for common data processing patterns--this book is indespensible for anyone using Hadoop."
--Tom White, author of Hadoop: The Definitive Guide
"Synopsis" may belong to another edition of this title.
About the Author
Adam Shook is a Software Engineer at ClearEdge IT Solutions, LLC, working with a number of big data technologies such as Hadoop, Accumulo, Pig, and ZooKeeper. Shook graduated with a B.S. in Computer Science from the University of Maryland Baltimore County (UMBC) and took a job building a new high-performance graphics engine for a game studio. Seeking new challenges, he enrolled in the graduate program at UMBC with a focus on distributed computing technologies. He quickly found development work as a U.S. government contractor on a large-scale Hadoop deployment. Shook is involved in developing and instructing training curriculum for both Hadoop and Pig. He spends what little free time he has working on side projects and playing video games.
"About the title" may belong to another edition of this title.
Revaluation Books
Exeter, United Kingdom
AbeBooks seller since January 6, 2003
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